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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Papale2025.IT-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x48d_wsl	.632
2	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.618
3	resnet50-moclr8deg	.612
4	resnext101_32x16d_wsl	.612
5	dinov2_vitl14_lc	.612
6	resnext101_32x32d_wsl	.596
7	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.594
8	convnext_xxlarge_clip	.592
9	resnet50-SIN_IN_IN	.592
10	antialiased-r50	.590
11	focalnet_tiny_lrf_in1k	.590
12	resnet_50_v1_spiking	.587
13	resnet_50_v1	.587
14	resnet50-sup	.587
15	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.586
16	vit_large_patch14_clip_224:openai_ft_in1k	.584
17	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.583
18	resnet50-vitoimagevidnet8	.581
19	resnet50-VITO-8deg-cc	.581
20	Res2Net50_26w_4s	.581
21	resnet50-SIN_IN	.579
22	resnet50_ewconplus_wzc	.576
23	densenet-121	.575
24	convnext_base:clip_laiona_augreg_ft_in1k_384	.574
25	voneresnet-50-non_stochastic	.572
26	dinov2_vitl14_reg_lc_featread	.571
27	dinov2_vitl14_reg_lc	.571
28	convnext_tiny_sup	.571
29	resnet50_robust_l2_eps1	.567
30	AdvProp_efficientnet-b7	.565
31	vit_base_patch16_clip_224:openai_ft_in1k	.565
32	deit_large_imagenet_full_seed-0	.564
33	voneresnet-50	.564
34	resnet50_tutorial	.563
35	vit_relpos_base_patch16_clsgap_224:sw_in1k	.562
36	ReAlnet03_cornet	.562
37	AdvProp_efficientnet-b6	.560
38	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.560
39	VOneCORnet-S	.559
40	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.559
41	ReAlnet01_cornet	.557
42	ReAlnet04_cornet	.557
43	efficientnet_b0	.556
44	convnext_tiny:in12k_ft_in1k	.554
45	AdvProp_efficientnet-b4	.550
46	pnasnet_large_halfPrecision	.548
47	resnet_SIN_IN_FT_IN	.547
48	resnet152_imagenet_full	.546
49	AT_efficientnet-b2	.545
50	AdvProp_efficientnet-b2	.545
51	vit_large_patch14_clip_224:laion2b_ft_in1k	.545
52	convnext_large_imagenet_full_seed-0	.543
53	shufflenet_v2_x1_0	.542
54	efficientnet_b2_imagenet_full	.540
55	resnet50_st_wzc_seed3_2_323	.538
56	convnext_large:fb_in22k_ft_in1k	.536
57	AdvProp_efficientnet-b8	.536
58	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.535
59	deit_base_imagenet_full_seed-0	.534
60	convnext_base_imagenet_full_seed-0	.534
61	resnet50_st_wzc_seed6_2_323	.532
62	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.531
63	resnet50_st_wzc_seed7_2_323	.530
64	ViT_L_32_imagenet1k	.528
65	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.527
66	resnet50_ewconplus_wzc_seed5_2__323	.526
67	deit_small_imagenet_full_seed-0	.525
68	resnet50_imagenet_full	.524
69	resnet50_ewconplus_wzc_seed6_2_323	.524
70	effnetb1_272x240	.523
71	resnet50_ewconplus_wzc_seed2_2__323	.523
72	mobilenet_v2_1_4_224	.521
73	convnext_femto_ols:d1_in1k	.520
74	resnext101_32x8d_wsl	.507
75	predictive_coding_pc	.506
76	resnet50-SIN	.505
77	resnet_50_v2	.504
78	custom_model_cv_18_dagger_408	.504
79	resnet-50-robust	.502
80	cvt_cvt-13-384-in1k_4_LucyV4	.498
81	cvt_cvt-21-384-in1k_4_LucyV4	.495
82	AlexNet_SIN_fov12	.490
83	convnext_small_imagenet_full_seed-0	.483
84	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.479
85	cvt_cvt-13-224-in1k_4_LucyV4	.476
86	AlexNet_SIN_fov16	.475
87	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.467
88	convnext_tiny_imagenet_full_seed-0	.463
89	convnext_xlarge:fb_in22k_ft_in1k	.459
90	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.453
91	grcnn	.448
92	convnext_base_fb_in1k	.442
93	cvt_cvt-21-224-in1k_4_LucyV4	.440
94	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.423
95	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
96	pr1_stl10_disentangled_alpha04	.401
97	AlexNet_SIN	.353
98	ReAlnet01_cornet_fov4	.316
99	resnet18_fair	.279
100	hmax	.171
101	alexnet_training_seed_02	.164
102	alexnet_training_seed_09	.159
103	alexnet_training_seed_03	.155
104	alexnet_training_seed_01	.151
105	alexnet_training_seed_10	.140
106	alexnet_training_seed_06	.135
107	alexnet_training_seed_07	.124
108	compact_resnet50_robust_V4	.066
109	vit_huge_clip336_laion
110	vit_large_clip336_openai
111	dinov2_vitl14_lc_res336
112	eva02_large_in22k
113	dinov2_vitg14_reg_lc
114	dinov2_vitg14_lc
115	cvt_cvt-w24-384-in22k_finetuned-in1k_4
116	vit_large_patch14_clip_336:openai_ft_in12k_in1k
117	vit_large_patch14_clip_336:laion2b_ft_in1k

Benchmark bibtex

@article{papale_extensive_2025,
	title = {An extensive dataset of spiking activity to reveal the syntax of the ventral stream},
	volume = {113},
	issn = {08966273},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S089662732400881X},
	doi = {10.1016/j.neuron.2024.12.003},
	journal = {Neuron},
	author = {Papale, Paolo and Wang, Feng and Self, Matthew W. and Roelfsema, Pieter R.},
	year = {2025},
}

Ceiling

0.98.

Note that scores are relative to this ceiling.

Data: Papale2025.IT

Metric: ridgecv